John A. Lunde

763 total citations
19 papers, 650 citations indexed

About

John A. Lunde is a scholar working on Molecular Biology, Cellular and Molecular Neuroscience and Cardiology and Cardiovascular Medicine. According to data from OpenAlex, John A. Lunde has authored 19 papers receiving a total of 650 indexed citations (citations by other indexed papers that have themselves been cited), including 17 papers in Molecular Biology, 6 papers in Cellular and Molecular Neuroscience and 5 papers in Cardiology and Cardiovascular Medicine. Recurrent topics in John A. Lunde's work include Muscle Physiology and Disorders (12 papers), Nerve injury and regeneration (4 papers) and Exercise and Physiological Responses (4 papers). John A. Lunde is often cited by papers focused on Muscle Physiology and Disorders (12 papers), Nerve injury and regeneration (4 papers) and Exercise and Physiological Responses (4 papers). John A. Lunde collaborates with scholars based in Canada, France and United States. John A. Lunde's co-authors include Bernard J. Jasmin, Vladimir Ljubicic, Robin J. Parks, Pedro Miura, Shihab E.O. Khogali, B. J. Jasmin, Julie Renaud, Michael Burt, Anthony O. Gramolini and Guy Bélanger and has published in prestigious journals such as Nucleic Acids Research, Journal of Biological Chemistry and Journal of Neuroscience.

In The Last Decade

John A. Lunde

19 papers receiving 643 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
John A. Lunde Canada 14 570 175 120 116 103 19 650
Sophie Currier United States 7 682 1.2× 94 0.5× 141 1.2× 156 1.3× 26 0.3× 7 811
Liam C. Hunt United States 17 415 0.7× 158 0.9× 79 0.7× 47 0.4× 65 0.6× 25 588
Kelly J. Perkins United Kingdom 10 479 0.8× 153 0.9× 48 0.4× 80 0.7× 45 0.4× 13 559
Kristine O'Brien United States 9 582 1.0× 100 0.6× 57 0.5× 43 0.4× 32 0.3× 9 638
Makoto Kunishige Japan 17 447 0.8× 119 0.7× 192 1.6× 51 0.4× 19 0.2× 38 820
Burcu Balcı-Hayta Türkiye 10 383 0.7× 53 0.3× 75 0.6× 50 0.4× 24 0.2× 19 489
John Hildyard United Kingdom 12 775 1.4× 81 0.5× 47 0.4× 197 1.7× 21 0.2× 23 856
Daniel Beltrán Valero de Bernabé United States 6 469 0.8× 93 0.5× 84 0.7× 77 0.7× 30 0.3× 6 556
Tadeusz Marciniec Poland 7 524 0.9× 90 0.5× 59 0.5× 27 0.2× 40 0.4× 9 603
Yaobin Jing China 7 309 0.5× 103 0.6× 21 0.2× 28 0.2× 20 0.2× 10 518

Countries citing papers authored by John A. Lunde

Since Specialization
Citations

This map shows the geographic impact of John A. Lunde's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by John A. Lunde with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites John A. Lunde more than expected).

Fields of papers citing papers by John A. Lunde

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by John A. Lunde. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by John A. Lunde. The network helps show where John A. Lunde may publish in the future.

Co-authorship network of co-authors of John A. Lunde

This figure shows the co-authorship network connecting the top 25 collaborators of John A. Lunde. A scholar is included among the top collaborators of John A. Lunde based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with John A. Lunde. John A. Lunde is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

19 of 19 papers shown
1.
Neault, Nafisa, Aymeric Ravel‐Chapuis, Stephen Baird, et al.. (2023). Vorinostat Improves Myotonic Dystrophy Type 1 Splicing Abnormalities in DM1 Muscle Cell Lines and Skeletal Muscle from a DM1 Mouse Model. International Journal of Molecular Sciences. 24(4). 3794–3794. 5 indexed citations
2.
Amirouche, Adel, Vanessa E. Jahnke, John A. Lunde, et al.. (2016). Muscle-specific microRNA-206 targets multiple components in dystrophic skeletal muscle representing beneficial adaptations. American Journal of Physiology-Cell Physiology. 312(3). C209–C221. 20 indexed citations
3.
Karmouch, Jennifer, et al.. (2015). HuR Mediates Changes in the Stability of AChR  -Subunit mRNAs after Skeletal Muscle Denervation. Journal of Neuroscience. 35(31). 10949–10962. 13 indexed citations
4.
5.
Ljubicic, Vladimir, et al.. (2014). Resveratrol induces expression of the slow, oxidative phenotype in mdx mouse muscle together with enhanced activity of the SIRT1-PGC-1α axis. American Journal of Physiology-Cell Physiology. 307(1). C66–C82. 81 indexed citations
6.
7.
Amirouche, Adel, Pedro Miura, Guy Bélanger, et al.. (2013). Converging pathways involving microRNA-206 and the RNA-binding protein KSRP control post-transcriptionally utrophin A expression in skeletal muscle. Nucleic Acids Research. 42(6). 3982–3997. 24 indexed citations
8.
Ljubicic, Vladimir, Pedro Miura, Michael Burt, et al.. (2011). Chronic AMPK activation evokes the slow, oxidative myogenic program and triggers beneficial adaptations in mdx mouse skeletal muscle. Human Molecular Genetics. 20(17). 3478–3493. 137 indexed citations
9.
Miura, Pedro, Joe V. Chakkalakal, John A. Lunde, et al.. (2007). Activation of PPARδ stimulates utrophin A expression in skeletal muscle cells. The FASEB Journal. 21(6). 1 indexed citations
10.
Perkins, Kelly J., Murat T. Budak, Santhosh M. Baby, et al.. (2007). Ets-2 Repressor Factor Silences Extrasynaptic Utrophin by N-Box–mediated Repression in Skeletal Muscle. Molecular Biology of the Cell. 18(8). 2864–2872. 18 indexed citations
11.
Deschênes‐Furry, Julie, Guy Bélanger, James Mwanjewe, et al.. (2005). The RNA-binding Protein HuR Binds to Acetylcholinesterase Transcripts andRegulates Their Expression in Differentiating Skeletal MuscleCells. Journal of Biological Chemistry. 280(27). 25361–25368. 40 indexed citations
12.
13.
Zhang, Yihong, John A. Lunde, Anthony Scimè, et al.. (2004). Ste20‐like kinase SLK displays myofiber type specificity and is involved in C2C12 myoblast differentiation. Muscle & Nerve. 29(4). 553–564. 20 indexed citations
14.
Bramson, Jonathan L., Natalie Grinshtein, John A. Lunde, et al.. (2004). Helper-Dependent Adenoviral Vectors Containing Modified Fiber for Improved Transduction of Developing and Mature Muscle Cells. Human Gene Therapy. 15(2). 179–188. 18 indexed citations
15.
Jasmin, Bernard J., Lindsay Angus, Guy Bélanger, et al.. (2002). Multiple regulatory events controlling the expression and localization of utrophin in skeletal muscle fibers: insights into a therapeutic strategy for Duchenne muscular dystrophy. Journal of Physiology-Paris. 96(1-2). 31–42. 27 indexed citations
16.
Matar, Wadih Y., John A. Lunde, Bernard J. Jasmin, & Jean‐Marc Renaud. (2001). Denervation enhances the physiological effects of the KATPchannel during fatigue in EDL and soleus muscle. American Journal of Physiology-Regulatory, Integrative and Comparative Physiology. 281(1). R56–R65. 11 indexed citations
17.
Jasmin, Bernard J., Anthony O. Gramolini, Feisal A. Adatia, et al.. (1998). Nerve-Derived Trophic Factors and DNA Elements Controlling Expression of Genes Encoding Synaptic Proteins in Skeletal Muscle Fibers. Canadian Journal of Applied Physiology. 23(4). 366–376. 2 indexed citations
18.
Gramolini, Anthony O., Edward A. Burton, Jonathon M. Tinsley, et al.. (1998). Muscle and Neural Isoforms of Agrin Increase Utrophin Expression in Cultured Myotubes via a Transcriptional Regulatory Mechanism. Journal of Biological Chemistry. 273(2). 736–743. 81 indexed citations
19.
Jasmin, Bernard J., Hala S. Alameddine, John A. Lunde, et al.. (1995). Expression of utrophin and its mRNA in denervated mdx mouse muscle. FEBS Letters. 374(3). 393–398. 29 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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